Mixed-Precision In- Memory Computing. arXiv preprint arXiv:1701.04279.Le Gallo M, Sebastian A, Mathis R, Manica M, Tuma T, Bekas C, Curioni A and Eleftheriou E 2017 arXiv.org arXiv:1701.04279 (Preprint 1701.0427
Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to...
Mixed-Precision In-Memory Computing Manuel Le Gallo*,1, 2 Abu Sebastian*,1 Roland Mathis,1 Matteo Manica,1, 2 Heiner Giefers,1 Tomas Tuma,1 Costas Bekas,1 Alessandro Curioni,1 and Evangelos Eleftheriou1 1)IBM Research - Zurich, 8803 Ru¨schlikon, Switzerland 2)ETH Zurich, 8092 Zurich,...
Here, we propose a mixed-precision architecture that combines a computational memory unit performing the weighted summations and imprecise conductance updates with a digital processing unit that accumulates the weight updates in high precision. A combined hardware/software training experiment of a ...
Here we introduce the concept of mixed-precision in-memory computing, which combines a von Neumann machine with a computational memory unit. In this hybrid system, the computational memory unit performs the bulk of a computational task, while the von Neumann machine implements a backward method to...
Execution time can be sensitive to memory or arithmetic bandwidth. Half-precision halves the number of bytes accessed, thus reducing the time spent in memory-limited layers. NVIDIA GPUs offer up to 8x more half precision arithmetic throughput when compared to single-precision, thus speeding up math...
Single-precision traininguses the 32-bit floating point (FP32) format. It's highly precise and is the standard in deep learning but can be computationally expensive. Half-precision traininguses the 16-bit floating point (FP16) format. It's faster and requires less memory than FP32...
“Automated mixed precision powered by NVIDIA Tensor Core GPUs on Alibaba allows us to instantly speedup AI models nearly 3X. Our researchers appreciated the ease of turning on this feature to instantly accelerate our AI.” — Wei Lin,Senior Director at Alibaba Computing Platform, Alibaba ...
For 8-bit input/output matrix–vector multiplications, in the four-phase (high-precision) or one-phase (low-precision) operational read mode, the chip can achieve a maximum throughput of 16.1 or 63.1 tera-operations per second at an energy efficiency of 2.48 or 9.76 tera-operations ...
Larger deep learning models need more computing power and memory resources. Faster training of deep neural networks has been achieved via the development of new techniques. Instead of FP32 (full-precision floating-point numbers format), you may use FP16 (half-precision floating-point numbers format...